History repeats itself. If you don’t evolve, you are going to be history. Dodo is an extinct species of a flightless bird. It’s commonly believed that the dodo went extinct around 1598 because Dutch sailors ate the beast to extinction. The bird was incredibly easy to catch because it had no fear of humans.
Don’t you think something similar is happening with automation? Tech-resistant enterprises face imminent danger.
Automation matters. It all comes down to this: you either play along and use it to level the field or choose to go extinct.
The more useful automation grows, the more terminology there is to go along with it. Let’s demystify the jargon that envelopes business process automation.
Robotic Process Automation
What is RPA?
RPA (Robotic Process Automation) is simply a technology that utilizes software robots that imitate human responses to complete repetitive and monotonous assignments.
The effectiveness of RPA lies in its ability to complete any repetitive, rule-based task at machine speed.
A simple example would be data updates. The HR team may need to update personnel data that is continuously changing. Software robots can be configured to automatically capture and update data via e-mail or forms – making sure the data is always fresh and relevant.
What is Artificial Intelligence?
Artificial Intelligence is the ability and architecture of making machines behave in ways that, until recently, were perceived only for human intelligence.
The final aim is to make computer programs solve problems and achieve goals in the world as well as humans.
The challenge, however, is that our perception of human intellect and our expectations of technology are continually evolving. As an example, in the 1950s, Chess was viewed as a unique challenge for artificial intelligence- not considered so today. Comparatively, today it has evolved to detect complicated health diseases, self-driving cars, and processing voice command.
PwC forecasts that AI could contribute up to $15.7 trillion to the global economy by 2030. While the AI hype is here, it’s time to break-down some of the more common terms that make up AI. RPA and Machine Learning can be the starting point for Artificial Intelligence.
What is Machine Learning?
Machine learning is the scope of knowledge that gives computers the ability to study without being explicitly programmed. It makes them similar to humans by giving them the ability to learn.
Types of Machine Learning
Supervised Machine Learning:
There is a training process. Sample inputs and desired outputs are fed into the computer model. The objective is to adopt a general rule that maps data to outputs. The training is complete when the model achieves a desired level of accuracy on the training data.
You feed the computer with historical market data and train the network to predict future price trends.
Unsupervised Machine Learning:
The learning algorithm does not have any labels, leaving it on its own to find structure in its input. Unsupervised learning can be an end objective in itself.
Clustering is an important concept when it comes to unsupervised learning. It mainly deals with finding a structure or pattern in a collection of uncategorized data. Clustering algorithms process your data and find natural clusters(groups) if they exist in the data.
When a computer program interacts with a fluid environment where a goal must be met, the application receives rewards and punishments as it steers through a problem scope- which is reinforcement learning.
Siri uses machine-learning technology to get smarter and develop the capability to understand natural language inquiries and demands. It is undoubtedly one of the most iconic examples of machine learning abilities of gizmos.
What is Deep Learning?
In 2016, Google’s AlphaGo software beat the human world champion at the board game “Go” based on deep learning. Since then, deep learning began appearing in news reports more frequently.
Deep Learning takes Machine learning a few steps ahead. It creates folds called a neural network- simulates the way a human brain works to operate beyond the initial decision point. Deep learning takes the result of the first machine learning decision and makes it the input for the subsequent machine learning decision.
Gartner placed deep neural nets (another term for deep learning) at the very top of its most recent Hype Cycle for Data Science and Machine Learning. Primary interests around Deep Learning have likely peaked. The next scene is unraveling this fantasy as businesses strive to turn the technology into something valuable.
What is Natural Language Processing?
Natural language processing intends to train machines in the human language. Without a doubt, there is a lot of advantage and productivity that emanates from it. By using NLP, developers can combine and structure knowledge to perform assignments such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation.
Here is an example of how Facebook uses NLP to turn social news feed into a personal newspaper- understand how Trending gets personalized. Facebook, using Graph Search as an NLP tool parses strings and figures out which strings are referring to nodes- objects in the network. To explain- I am a node; my friendship with my colleague Random is an edge. Random’s “like” of pilates is an edge; Pilates is a node. Do you get the gist?
All those strings get parsed into what Facebook calls entities — nodes in the network — including people, places, things, events, topics. Moreover, each node has many edges, such as Likes, check-ins, hashtags, comments.
Graph Search operates based on a thorough understanding of these nodes and edges based on NLP to build your personalized trends and feeds.
What is Computer Vision?
Computer vision is a field of computer science that operates on enabling computers to see, classify, and process images in the same way that human vision does, and then provide relevant output. It is like granting human intelligence and instincts to a computer. In reality, though, it is a tough task to empower computers to recognize images of various objects.
For example, vehicles with computer vision would be able to classify and detect objects on and around the road, such as traffic lights, pedestrians, traffic signs, and act accordingly. The intelligent device could provide inputs to the driver or even make the car halt if there is an unforeseen obstacle on the road.
What is TensorFlow?
A Tensor is an algebraic object related to a vector space- represented as an organized multidimensional array. It is described Tensorflow because it takes input as a multidimensional array. Simply put, one can create a kind of a flowchart of transactions (called a Graph) that you want to perform on that data. The input goes in at one end, and then it flows through this system of multiple operations and comes out the other end as output.
Called TensorFlow because the tensor goes in it flows through a list of operations, and then it comes out the other side.
Google Brain first formulated TensorFlow for internal use. TensorFlow is a library developed to accelerate machine learning and deep neural network research. If the user types a keyword in the search bar, Google provides a prompt about what could be the next word. Google wants to use machine learning to take advantage of their massive datasets to give users the best experience.
It was later released to open source. It works within an environment of tools, archives, and community resources. It supports businesses to build and deploy machine learning-powered applications.
Here’s an example of how Airbnb improved the guest experience by using TensorFlow to classify images and detect objects at scale.